Far and near field mixed source off-grid positioning method based on sparse Bayesian learning

The invention discloses a far and near field mixed source off-grid positioning method based on sparse Bayesian learning, and belongs to the field of underwater acoustic detection. The problem that high positioning precision and high calculation efficiency cannot be achieved at the same time through an existing method is solved. According to the method, the far and near field off-network model is constructed, and the far and near field off-network error is introduced into the sparse Bayesian learning process as a hyper-parameter, so that effective estimation and compensation of the off-network error are realized, higher-precision far and near field positioning is completed, and the influence of near field strong interference on far field direction finding is greatly weakened. And meanwhile, the far and near field grid evolution technology is utilized to realize autonomous split learning of far and near field grid points near a target position, so that the grid points can cover an interested airspace in a non-u